Enterprise AI Solutions for the Alabama Board of Medical Examiners | May 2026
BME Internal AI is a fully on-premises artificial intelligence system deployed by the Board of Medical Examiners IT Department. It provides a secure, agency-controlled AI assistant for BME and MLC staff — operating entirely on hardware owned and managed by the agency, with no data transmitted to external cloud services.
Staff access the system through a web browser at https://ai.local.albme.org using their existing Windows network credentials. The system is restricted to staff members assigned to the Ollama security group in Active Directory.
Machine
Dell Pro Max Tower T2 Workstation
CPU
Intel Core Ultra 9 285 — 24 cores, 5.6 GHz max, 65W TDP
GPU
NVIDIA RTX PRO 6000 Blackwell — 96GB GDDR7, 300W TDP
RAM
128GB DDR5 4400 MT/s (56GB allocated to AI stack, 64GB to Windows)
Storage
2x 2TB NVMe PCIe Gen4 in RAID 1
Power Supply
1500W 80 Plus Platinum (operating at ~30% load)
Host OS
Windows 11 Pro (work) + Ubuntu 24.04 in WSL2 (AI stack)
Network
10.0.10.89 on agency LAN via ai.local.albme.org DNS A record
Open-source inference server that runs large language models locally. It manages GPU memory, handles model loading and unloading, and exposes an OpenAI-compatible API. Runs as a system service with automatic startup.
Why: Simplest path to running local models with full GPU support, OpenAI-compatible API, and the largest model library of any local inference tool.
Google DeepMind's flagship model released April 2026 under Apache 2.0 license. 31 billion parameters consuming ~20GB of 96GB available GPU memory. Features native vision capability, 256,000-token context window, strong reasoning and knowledge performance.
Why: Current-generation model with vision capabilities, permissive Apache 2.0 license, and Western development meeting agency requirements.
Open-source web application providing the chat interface at ai.local.albme.org. Handles user authentication, conversation history, model selection, document uploads, and knowledge base management via Docker container.
Why: Multi-user with role-based permissions, built-in LDAP/AD integration, custom workspaces, active development, MIT licensed.
High-performance vector database storing indexed document representations. Currently: 14,784 files indexed, 152,000+ chunks stored with metadata (filename, PII flags, entity types, processing date). Supports both semantic and keyword-based filtering.
Why: Designed for vector search at scale, efficient local hardware operation, Open WebUI's recommended external vector store.
Docker-based reverse proxy handling all incoming HTTPS traffic. Self-signed SSL certificate for ai.local.albme.org deployed via Group Policy to domain-joined machines. Automatic HTTP → HTTPS redirect.
Custom Python pipeline processes documents from file server shares into Qdrant. Runs as systemd service with automatic scheduling: 4 workers 6pm–6am, 1 worker during business hours. Fully resumable via SQLite checkpoint database.
Extracts text from PDFs (including scanned via GPU-accelerated OCR), Word, Excel.
Scans extracted text for PII. Detected entity types (SSN, names, addresses, phone, license numbers, etc.) stored as searchable metadata.
Converts text chunks into 768-dimensional vectors via Ollama API for semantic search.
Stores vectors and metadata. Queryable via semantic similarity or exact keyword matching.
File Server: \\10.0.0.29 (Windows Server 2016) mounted in WSL via CIFS. Service account: svc-fileingest (read-only). Current share: bme (160GB, 14,784 files). Planned: legal, investigations, credentialing, mlc.
Custom Python tool integrated into Open WebUI gives the AI model the ability to search Qdrant during conversations. Two search modes:
Semantic Search (search_documents)
Converts user question to vector, finds conceptually similar chunks. Best for topics, regulations, general research.
Keyword Search (keyword_search)
Finds exact text matches via full-text index. Essential for names, case numbers, dates, specific terms.
The AI model selects the appropriate search mode based on the nature of the question, guided by system prompt instructions.
Regulatory Q&A
Ask questions about Alabama Admin Code Title 540/545 and Code of Alabama Title 34 Chapter 24 with cited answers.
Document Search
Search BME document archive (14,784 documents indexed) by topic, names, or specific terms.
Writing Assistance
Draft emails, letters, memos, reports, policy documents. Formal outputs are stamped to indicate AI assistance.
General Research
Research topics, summarize documents, answer general questions, assist with data analysis and spreadsheets.
Code Development
IT staff can use the system for software development assistance through VS Code integration.
All data remains on agency hardware. No external LLM API calls. Web search is the only outbound activity.
Active Directory LDAP. Access controlled by Ollama security group. First-login approval required.
TLS 1.2/1.3 via Nginx. Certificate deployed via GPO to domain machines.
Windows BitLocker on host. WSL2 virtual disk on RAID 1 NVMe.
Windows Defender on host. No additional AV exclusions required.
Western-developed models only (Google, OpenAI, Meta). No Chinese-developed models.
Microsoft Presidio scans all document chunks. Entity types stored as searchable metadata.
Hard limits on probable cause determinations, charging decisions, and adjudications. BME AI Use Policy enforced automatically in system prompt.
The current deployment is a proof-of-concept on a single workstation. The architecture is designed to scale. Future capabilities under consideration: